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3D Human Tracking with Catadioptric Omnidirectional Camera

Communication avec acte
Auteur
ABABSA, Fakhreddine
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
HADJ-ABDELKADER, Hicham
143247 Informatique, Biologie Intégrative et Systèmes Complexes [IBISC]
BOUI, Marouane
143247 Informatique, Biologie Intégrative et Systèmes Complexes [IBISC]

URI
http://hdl.handle.net/10985/16195
DOI
10.1145/3323873.3325027
Date
2019

Résumé

This paper deals with the problem of 3D human tracking in catadioptric images using particle-filtering framework. While traditional perspective images are well exploited, only a few methods have been developed for catadioptric vision, for the human detection or tracking problems. We propose to extend the 3D pose estimation in the case of perspective cameras to catadioptric sensors. In this paper, we develop an original likelihood functions based, on the one hand, on the geodetic distance in the spherical space SO3 and, on the other hand, on the mapping between the human silhouette in the images and the projected 3D model. These likelihood functions combined with a particle filter, whose propagation model is adapted to the spherical space, allow accurate 3D human tracking in omnidirectional images. Both visual and quantitative analysis of the experimental results demonstrate the effectiveness of our approach.

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LISPEN_ICMR_ABABSA_2019.pdf
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Fin d'embargo:
2019-12-31
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  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)

Documents liés

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  • 3D Human Pose Estimation with a Catadioptric Sensor in Unconstrained Environments Using an Annealed Particle Filter 
    Article dans une revue avec comité de lecture
    ABABSA, Fakhreddine; HADJ-ABDELKADER, Hicham; BOUI, Marouane (MDPI, 2020)
    The purpose of this paper is to investigate the problem of 3D human tracking in complex environments using a particle filter with images captured by a catadioptric vision system. This issue has been widely studied in the ...
  • Deep Learning for Additive Manufacturing-driven Topology Optimization 
    Article dans une revue avec comité de lecture
    ALMASRI, Waad; DANGLADE, Florence; BETTEBGHOR, Dimitri; ADJED, Faouzi; ABABSA, Fakhreddine (Elsevier BV, 2022-06-21)
    This paper investigates the potential of Deep Learning (DL) for data-driven topology optimization (TO). Unlike the rest of the literature that mainly applies DL to TO from a mechanical perspective, we developed an original ...
  • Towards improving the future of manufacturing through digital twin and augmented reality technologies 
    Article dans une revue avec comité de lecture
    RABAH, Souad; ASSILA, Ahlem; KHOURI, Elio; MAIER, Florian; ABABSA, Fakhreddine; BOURNY, Valéry; MAIER, Paul; MERIENNE, Frédéric (Elsevier, 2018)
    We are on the cusp of a technological revolution that will fundamentally change our relationships to others and the way we live and work. These changes, in their importance, scope, and complexity, is different than what ...
  • An Efficient Human Activity Recognition Technique Based on Deep Learning 
    Article dans une revue avec comité de lecture
    KHELALEF, Aziz; ABABSA, Fakhreddine; BENOUDJIT, Nabil (MAIK Nauka/Interperiodica (МАИК Наука/Интерпериодика), 2019)
    In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to ...
  • Augmented Reality Application in Manufacturing Industry: Maintenance and Non-destructive Testing (NDT) Use Cases 
    Communication avec acte
    ABABSA, Fakhreddine (Springer International Publishing, 2020)
    In recent years, a structural transformation of the manufacturing industry has been occurring as a result of the digital revolution. Thus, digital tools are now systematically used throughout the entire value chain, from ...

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